Just a few years ago, genetic testing was a whim available only to a few. However, in the evolving world of clinical research, genetic testing turned out to be a game-changer – constant development of these methods made them more available and used in many areas of medicine.
Genetic testing enables greater precision, efficiency, and success in clinical trials. As the demand for targeted therapies grows, particularly in oncology, neurology, and rare diseases, integrating genetic insights has moved from being optional to essential.
Recruiting the right patients through genetic testing
One of the most significant advantages of genetic testing in clinical trials is its ability to identify patients who are most likely to benefit from a given therapy. However, the benefit is not only for patients, but also for the sponsor of the study, as the more precise recruitment increases the chances for desirable test results. By analysing a participant’s DNA, researchers can pinpoint specific genetic mutations or biomarkers that correlate with disease susceptibility or treatment response.
Genetic testing is a great value in:
- Improving patient stratification – matching participants to therapies based on genetic biomarkers leads to a more homogeneous study population, increasing statistical power.
- Identifying responders vs. non-responders – genetic variants (e.g., BRCA1/2 in breast cancer or EGFR mutations in lung cancer) help predict which individuals are most likely to benefit from a treatment.
- Enhancing safety profiling – pharmacogenomics can detect genetic predispositions to adverse drug reactions, improving safety and reducing trial dropouts.
- Accelerating enrollment – precision recruitment through genetic screening allows faster identification of eligible patients, especially in trials targeting rare diseases.
As genetic testing continues to evolve, we can expect to see even more innovative applications in clinical trials, leading to improved treatment outcomes and better patient care.
When should genetic testing be planned?
Genetic testing in clinical trials should be planned strategically during the trial design phase, taking into consideration the trial’s objectives, patient population, and regulatory requirements.
How to integrate genetic testing:
- Pre-enrollment planning – identify patient subgroups that may respond differently to the treatment, and determine if genetic testing is an inclusion/exclusion criterion
- At screening – in oncology trials, patients may be screened before being assigned to a treatment arm.
- During the trial – correlate genetic variants with treatment response or adverse events.
The key point is to integrate genetic testing thoughtfully into the overall trial design stage rather than adding it as an afterthought, ensuring it serves clear scientific objectives and maintains participant safety and regulatory compliance.
Proven successes: Real-world applications of genetic testing
Genetic testing has been used successfully for a couple of years, improving medical therapies in diverse branches of medicine:
- Anti-virus therapy: HLA-B*57:01 testing is now standard before prescribing abacavir (an HIV medication) to prevent life-threatening hypersensitivity reactions. Such strategies not only improve trial safety but also lay the groundwork for personalised medicine in broader clinical practice.
- Blood clotting: CYP2C19 testing in patients receiving clopidogrel (a blood thinner). Individuals with specific genetic variants may not respond adequately to the drug, increasing their risk of cardiovascular events. Identifying these cases upfront ensures safer trial protocols and better patient care.
- Oncology: PARP inhibitors now routinely test for BRCA mutations to identify likely responders.
- Neurology: In Alzheimer’s research, APOE genotyping helps stratify risk groups and tailor therapeutic approaches.
- Anti-convulsant: Screening for HLA-B∗1502 before using carbamazepine can prevent Stevens–Johnson syndrome/toxic epidermal necrolysis in Asian populations.
- Rare diseases: Genetic confirmation of conditions like Duchenne muscular dystrophy (DMD) or spinal muscular atrophy (SMA) is a prerequisite for enrollment.
These successes underscore genetic testing’s role in driving efficient, effective clinical trials and transforming modern healthcare into a precision-based field. As costs decline and technologies advance, genetic testing will become a standard component of trial design, not just an add-on.
What’s next? AI-powered genetic testing in clinical trials
The integration of artificial intelligence (AI) with genetic testing is poised to revolutionise the world of clinical trials further. Different methods used in AI concerns, among others, association rule mining (discovering interesting relations between variables in large databases), brain–machine interface (direct communication pathway between an enhanced or wired brain and an external device), deep learning (use multiple layers to extract higher level features from raw input progressively) and machine learning (algorithms that build a mathematical model of sample data).
AI can be helpful in:
- Real-Time Analysis: AI integrated with next-generation sequencing could provide instant genetic test results during trials.
- Predict outcomes: Machine learning models can be used to predict clinical trial outcomes, potentially optimising trial design, accelerating drug development, and improving patient selection.
- Global Collaboration: AI platforms sharing anonymised genomic data across borders to accelerate rare disease research.
- CRISPR and AI: Combining gene-editing technologies with AI for dynamic, personalised therapy adjustments.
Despite its promise, the use of AI in genetic testing for clinical trials presents challenges. These include ensuring data privacy, addressing algorithmic bias (especially in underrepresented populations), and maintaining transparency in AI decision-making. Moreover, informed consent processes must evolve to ensure participants understand how their genetic data will be used and analysed by AI systems.
However, do not let these challenges overshadow the benefits. Mainly, incorporation of AI into clinical trials leads to more cost-effective studies, e.g. by excluding non-responsive patients at an early stage of the trial, which results in improved success rate and reduced adverse event management costs. Other benefits include: speeding up genetic analysis from weeks to hours, and the possibility of processing large datasets.
While the benefits are clear, challenges remain. Genetic research has historically underrepresented diverse populations, risking biased outcomes. Additionally, data privacy and ethical considerations around genetic information must be addressed. The field needs robust data security protocols and collaboration among researchers, clinicians, and policymakers.
As we stand on the cusp of the genetic revolution, one thing is clear personalised medicine is not a trend, but a transformative paradigm that will continue to redefine healthcare for generations to come.
How can we support you?
We help biopharma companies integrate genetic testing into their clinical development strategies by:
- support in the planning of the clinical trial assumptions,
- preparation or verification of essential study documents,
- search and selection of clinical sites
Genetic testing is a powerful tool that can unlock the full potential of drug candidates. For companies aiming to maximise value with targeted, science-driven development, incorporating genetic testing early in clinical design is no longer optional – it’s a strategic advantage.
References:
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- Hanson AJ, Craft S, Banks WA. The APOE genotype: modification of therapeutic responses in Alzheimer’s disease. Curr Pharm Des. 2015;21(1):114-20.
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- Lee CR, Luzum JA, Sangkuhl K, Gammal RS, Sabatine MS, Stein CM, Kisor DF, Limdi NA, Lee YM, Scott SA, Hulot JS, Roden DM, Gaedigk A, Caudle KE, Klein TE, Johnson JA, Shuldiner AR. Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2C19 Genotype and Clopidogrel Therapy: 2022 Update. Clin Pharmacol Ther. 2022 Nov;112(5):959-967.
- Locharernkul C, Shotelersuk V, Hirankarn N. Pharmacogenetic screening of carbamazepine-induced severe cutaneous allergic reactions. J Clin Neurosci. 2011 Oct;18(10):1289-94.
- Mounzer, K., Hsu, R., Fusco, J.S. et al. HLA-B*57:01 screening and hypersensitivity reaction to abacavir between 1999 and 2016 in the OPERA® observational database: a cohort study. AIDS Res Ther 2019, 16, 1.
- Varshney, Nitin & Patidar, Nandkishore & Dixit, Sheetal & Jain, Rahul. (2025). AI-Powered CRISPR: Revolutionizing Precision Medicine and Genomic Therapeutics. American Journal of Biomedical Science & Research. 26. 183-188.